Neural Ray Tracing With Confidence Maps for Artifact Reduction
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Solution Overview
Problem
Existing 3D rendering techniques face challenges in achieving high-quality 2D images with reduced artifacts while managing computational complexity, particularly in ray tracing processes.
Innovation Solution
A method utilizing neural sampling map generation and reconstruction models to warp and process image frames, generating confidence maps and sampling maps to optimize ray tracing based on confidence scores and limited sampling numbers, thereby improving image quality and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ray tracing is performed with a fixed number of light rays for each pixel, then the rendering process is simple and fast, but image quality deteriorates due to artifacts and insufficient detail
Solution Approach 1:
The patent applies dynamics by making the number of light rays adaptive rather than fixed. The neural network dynamically determines the optimal number of rays to trace for each pixel based on the confidence map, allowing the rendering process to adjust its complexity locally. This resolves the contradiction by enabling high image quality where needed while maintaining simplicity where possible.
Solution Approach 2:
The patent implements local quality by applying different rendering strategies to different regions of the image. High-confidence regions use fewer rays while low-confidence regions use more rays, as determined by the neural network's analysis of the confidence map. This localized approach improves overall image quality without uniformly increasing computational complexity across the entire image.
2Manufacturing precision
If the number of light rays is increased to reduce artifacts, then image quality improves, but computational efficiency deteriorates due to increased computational load
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the number of light rays as a variable parameter based on confidence scores. The neural network analyzes the confidence map and determines the optimal ray count for each pixel, changing this parameter adaptively rather than using a fixed high value. This resolves the contradiction by increasing rays only where necessary to reduce artifacts, thereby maintaining computational efficiency.
Solution Approach 2:
The patent implements partial action by applying ray tracing with varying intensity across different regions. Instead of uniformly tracing maximum rays across the entire image (excessive action), the system traces only the necessary number of rays for each region based on confidence levels (partial action). This reduces overall computational load while maintaining image quality in critical regions.
3Manufacturing precision
If uniform sampling is used for all pixels, then the rendering process is simple, but image quality deteriorates due to artifacts in low-confidence regions
Solution Approach 1:
The patent applies local quality by implementing adaptive sampling strategies for different pixel regions based on confidence scores. The neural network identifies low-confidence regions and applies enhanced sampling (more rays) specifically to those areas, while using standard sampling for high-confidence regions. This localized differentiation improves image quality without uniformly complicating the sampling process.
Solution Approach 2:
The patent implements feedback by using the confidence map generated during the rendering process to guide subsequent sampling decisions. The neural network analyzes the confidence scores from previous iterations and uses this feedback information to determine the optimal number of rays to trace for each pixel in the next iteration. This feedback mechanism enables continuous improvement of image quality while managing sampling complexity.
Data Source
AI summary
A method and apparatus with image processing is provided. The processor-implemented method includes generating a warped image frame by warping a first reconstructed image frame of a first time point based on first change data corresponding to a change between first rendered image frame of the first time point and second rendered image frame of a second time point that is different from the first time point; generating, using a neural reconstruction model based on the second rendered image frame and the warped image frame, a confidence map representing a second reconstructed image frame of the second time point and confidence scores of pixels of the second reconstructed image frame; and generating a third rendered image frame of a third time point, different from the first and second time points, by ray tracing for each of plural pixels of the third rendered image frame based on the confidence map.